Robot Speed Profiling for Safer Human-Robot Collaboration
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Solution Overview
Problem
Current human-robot collaboration methods for preplanned movement sequences in industrial settings face challenges in maintaining high productivity, safety, and low wear, while also ensuring user-friendliness, due to assumptions not always aligning with actual task performance and requiring high computational effort for complex calculations and sudden reactions.
Innovation Solution
A method that involves repeatedly running a preplanned movement sequence, monitoring for disturbances, and modifying speed development based on the frequency of detected disturbances to create an improved movement sequence that reduces violent reactions and increases productivity by adjusting speeds in sections with few or many disturbances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot moves at high speed along the preplanned movement path to increase productivity, then productivity is improved, but the risk of collision with humans increases
Solution Approach 1:
The robot's speed is dynamically adjusted based on real-time disturbance detection. The control device modifies the speed development from the preplanned movement sequence according to detected disturbances in the environment, allowing high speed in safe zones and reduced speed in zones with human presence or obstacles, thus resolving the contradiction between productivity and safety
Solution Approach 2:
The system continuously monitors the environment for disturbances and feeds this information back to the control device. Based on this feedback, the control device adapts the robot's speed in real-time, enabling the robot to maintain high productivity when the environment is clear while automatically reducing speed when disturbances are detected, thereby balancing productivity and safety
2Reliability
If the robot frequently deviates from the preplanned movement path to avoid hazards, then safety is improved, but wear on the robot increases due to sudden reactions
Solution Approach 1:
Instead of sudden path deviations, the system dynamically adjusts the speed development along the existing preplanned movement path. This gradual speed modulation avoids violent reactions and sudden directional changes, reducing mechanical wear while maintaining safety through continuous adaptation to environmental disturbances
Solution Approach 2:
The system prepares for potential hazards by continuously monitoring the environment and proactively adjusting speed before collisions occur. This preventive approach cushions against sudden impacts and violent reactions by gradually modifying movement parameters in anticipation of detected disturbances, thereby reducing wear while maintaining safety
3Reliability
If complex real-time calculations are performed to predict human behavior and adjust robot movement, then safety is improved, but computational effort and processing power increase
Solution Approach 1:
The system extracts only the essential information needed for safety - simple disturbance detection (presence/absence of obstacles or humans) - rather than performing complex full behavior prediction. This extracted minimal information is sufficient for speed adjustment while significantly reducing computational effort and processing requirements
Solution Approach 2:
The system changes the parameter being monitored from complex human behavior prediction to simple disturbance detection. By focusing on detecting whether disturbances exist rather than predicting detailed human intentions, the computational complexity is dramatically reduced while maintaining adequate safety through speed adaptation based on detected disturbance frequency
4Reliability
If the robot continuously monitors the environment for disturbances, then safety is improved, but processing time and complexity increase
Solution Approach 1:
The monitoring system extracts only essential disturbance information (presence, position, and basic movement of obstacles or humans) rather than comprehensively analyzing all environmental parameters. This extracted minimal data set is sufficient for speed adjustment decisions while keeping processing complexity manageable
Solution Approach 2:
The system performs partial monitoring focused specifically on disturbance detection relevant to safety, rather than comprehensive environmental analysis. This partial action approach monitors only the critical parameters needed for speed adaptation, reducing processing complexity while maintaining safety
Data Source
AI summary
For the control of a robot operated as part of a human-robot collaboration, a preplanned movement sequence is provided that comprises a defined movement path along which the robot is to move in order to perform a specific task, and a defined speed development according to which each point of the movement path is associated with a speed at which the robot is to move at the respective point. A method of improving the preplanned movement sequence comprises the following steps: The robot is controlled to repeatedly run through the preplanned movement sequence; during the repeated run-through of the preplanned movement sequence, an environment of the robot is monitored and, in so doing, disturbances are detected that emanate from objects that are located in this environment or that enter into this environment so that an endangerment of the respective object by the robot results therefrom; on the repeated run-through, for a plurality of different sections of the movement path, the frequency of disturbances detected in the respective section is determined; after the repeated run-through of the preplanned movement sequence, a modified speed development is determined in that, for the different sections, the speeds associated with the points of the respective section are modified in dependence on the frequency determined for the respective section; finally, an improved movement sequence that comprises the modified speed development is provided for the further control of the robot.